{"id":"W4250747312","doi":"10.1515/iupac.88.0235","title":"Countercurrent Chromatography (CCC)","year":2017,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Chromatography in Natural Products","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; National Research Council Canada","funders":"","keywords":"Countercurrent exchange; Extraction (chemistry); Computer science; Chromatography; Process engineering; Sample (material); Scale (ratio); Sample preparation; Throughput; Chemistry; Engineering; Physics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004572971,0.00116653,0.00133626,0.0004620837,0.0005102641,0.0004616813,0.002404535,0.001079116,0.008400846],"category_scores_gemma":[0.0008176672,0.001079771,0.0008384231,0.0002891388,0.0006703025,0.0002379285,0.0005846079,0.002071215,0.0000101482],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004369003,"about_ca_system_score_gemma":0.0008119383,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003511702,"about_ca_topic_score_gemma":0.0007800666,"domain_scores_codex":[0.9947441,0.00004150397,0.0008865856,0.001423463,0.001960745,0.0009436302],"domain_scores_gemma":[0.9933985,0.0000983879,0.001073713,0.004228473,0.0008295538,0.000371407],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000111203,0.0004375306,0.00004463503,0.001921718,0.0004913625,0.0002209365,0.00001227614,5.177398e-7,0.000209299,0.000003824699,0.9946324,0.001914245],"study_design_scores_gemma":[0.001181571,0.00006388654,0.00003067923,0.001621785,0.0003955478,0.0001132303,0.00002124051,0.00000523528,0.0005342701,0.0001483654,0.994729,0.001155167],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001825289,0.009734578,0.000001628421,0.0002288284,0.002459868,0.0002440473,0.9847684,0.000324072,0.0004133207],"genre_scores_gemma":[0.000590465,0.003342465,0.00004033769,0.0001024993,0.002686759,0.00003045439,0.9928883,0.0001129078,0.000205788],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.008390698,"threshold_uncertainty_score":0.9991652,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01565340875239613,"score_gpt":0.3991606518285604,"score_spread":0.3835072430761643,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}